The currency market features a relatively small cross-section and conditional expected returns can be characterized by only a few signals - interest differentials, trend and mean-reversion. Chernov, Dahlquist, and Lochstoer exploit these properties to construct a conditional projection of the stochastic discount factor onto excess returns of individual currencies. Their approach is implementable in real time and prices all currencies and prominent strategies conditionally as well as unconditionally. The researchers document that the fraction of unpriced risk in these assets is at least 85%. Extant explanations of carry strategies based on intermediary capital or global volatility are related to these unpriced components, while consumption growth is related to the priced component of returns.
In addition to the conference paper, the research was distributed as NBER Working Paper w28260, which may be a more recent version.
Bai and Massa study the degree of information substitutability in the financial market. Exploiting the cross-sectional and time-series variations of the pandemic-triggered lockdowns that have hampered people's interaction hence the ability to collect, process, and transmit soft information, the researchers investigate how the difficulty/inability to use soft information has prompted a switch to hard information, and further its implication on fund performance. Bai and Massa show that lockdown reduces fund investment in proximate stocks and generate a portfolio rebalancing towards distant stocks. The rebalancing has negative implications on fund performance by reducing fund raw (excess) return of an additional 0.76% (0.29%) per month during lockdown, suggesting that soft and hard information is not easily substitutable. They show that soft information originates mainly with geographic proximity and human interactions, mostly in café, restaurants, bars, and fitness centers. This suggests that the virtual world based on Zoom/Skype/Team has direct negative implications on the ability of collecting soft information and therefore affects strategies relying on them such as proximity investment.
Kahn and Barth document the rise and fall of an arbitrage trade among hedge funds known as the Treasury cash-futures basis trade. This trade exploited a fundamental disconnect between cash and futures prices of Treasuries. They show that in recent years a replicating portfolio of Treasury bills and futures has been overvalued relative to Treasury notes and bonds, creating an opportunity for arbitrageurs. Using regulatory datasets on hedge fund exposures and repo transactions, the researchers are able to both identify these arbitrage positions and estimate their aggregate size. They show that the basis trade became popular among hedge funds following 2016, rising to make up as much as half of all hedge fund Treasury positions and around a quarter of dealers' repo lending. Kahn and Barth present a model and empirical evidence that link the rise in the basis trade to broader developments in the Treasury market, and shows how the trade could contribute to financial instability. In March of 2020, many of the risks of the trade materialized as Treasury market illiquidity associated with the COVID-19 pandemic led to large sales of these basis trade positions among hedge funds. While Treasury market disruptions spurred hedge funds to sell Treasuries, the unwinding of the basis trade was likely a consequence rather than the primary cause of the stress. Prompt intervention by the Federal Reserve may have prevented the trade from accelerating the deterioration of Treasury market functioning. Their results underscore the importance of non-bank actors in the current structure of the Treasury market, and suggest this structure could create risks going forward.
Bryzgalova, Julliard, and Huang propose a novel framework for analyzing linear asset pricing models: simple, robust, and applicable to high dimensional problems. For a (potentially misspecified) standalone model, it provides reliable risk premia estimates of both tradable and nontradable factors, and detects those weakly identified. For competing factors and (possibly non-nested) models, the method automatically selects the best specification - if a dominant one exists - or provides a model averaging, if there is no clear winner given the data. The researchers analyze 2.25 quadrillion models generated by a large set of existing factors, and gain novel insights on the empirical drivers of asset returns.
Active mutual fund managers care about fund size, which is affected by common fund flows driven by macroeconomic shocks. Fund managers hedge against common flow shocks by tilting their portfolios toward low-flow-beta stocks. In equilibrium, common flow shocks earn a risk premium. A multi-factor asset pricing model similar to the ICAPM arises, even with all agents behaving myopically. Empirically, fund flows obey a strong factor structure with the common component earning a risk premium, and fund portfolios are, on average, tilted toward low-flow-beta stocks. This tilt increases in magnitude when flow-hedging motives strengthen following natural disasters and unexpected trade-war announcements.
Gabaix and Koijen develop a framework to theoretically and empirically analyze the fluctuations of the aggregate stock market. Households allocate capital to institutions, which are fairly constrained, for example operating with a mandate to maintain a fixed equity share or with moderate scope for variation. As a result, the price elasticity of demand of the aggregate stock market is small, so flows in and out of the stock market have large impacts on prices. Using the recent method of granular instrumental variables, the researchers find that investing $1 in the stock market increases the market's aggregate value by about $5. Gabaix and Koijen also show that they can trace back the time variation in the market's volatility to flows and demand shocks of different investors. The researchers also analyze how key parts of macro-finance change if markets are inelastic. Gabaix and Koijen show how general equilibrium models and pricing kernels can be generalized to incorporate flows, which makes them amenable to use in more realistic macroeconomic models, and to policy analysis. Their calibration implies that government purchases of equities have a non-trivial impact on prices. Corporate actions that would be neutral in a rational model, such as share buybacks, have substantial impacts too. Their framework allows us to give a dynamic economic structure to old and recent datasets comprising holdings and flows in various segments of the market. The mystery of apparently random movements of the stock market, hard to link to fundamentals, is replaced by the more manageable problem of understanding the determinants of flows in inelastic markets. Gabaix and Koijen delineate a research agenda that can explore a number of questions raised by this analysis, and might lead to a more concrete understanding of the origins of financial fluctuations across markets.